Examinando por Autor "Ghimire, Sujan"

Mostrando 1 - 6 de 6

A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
(Elsevier, 2023) Ghimire, Sujan; Nguyen-Huy, Thong; AL-Musaylh, Mohanad S.; Deo, Ravinesh C.; Casillas-Pérez, David; Salcedo-Sanz, Sancho
Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem.
Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction
(Elsevier, 2022) Ghimire, Sujan; Deo, Ravinesh C.; Casillas-Pérez, David; Salcedo-Sanz, Sancho; Sharma, Ekta; Ali, Mumtaz
Global solar radiation (GSR) prediction plays an essential role in planning, controlling and monitoring solar power systems. However, its stochastic behaviour is a significant challenge in achieving satisfactory prediction results. This study aims to design an innovative hybrid prediction model that integrates a feature selection mechanism using a Slime-Mould algorithm, a Convolutional-Neural-Network (CNN), a Long– Short-Term-Memory Neural Network (LSTM) and a final CNN with Multilayer-Perceptron output (SCLC algorithm hereafter). The proposed model was applied to six solar farms in Queensland (Australia) at daily temporal horizons in six different time steps. The comprehensive benchmarking of the obtained results with those from two Deep-Learning (CNN-LSTM, Deep-Neural-Network) and three Machine-Learning (ArtificialNeural-Network, Random-Forest, Self-Adaptive Differential-Evolutionary Extreme-Learning-Machines) models highlighted a higher performance of the proposed prediction model in all the six selected solar farms. From the results obtained, this work establishes that the designed SCLC algorithm could have a practical utility for applications in renewable and sustainable energy resource management.
Electricity demand uncertainty modeling with Temporal Convolution Neural Network models
(Elsevier, 2025-03) Ghimire, Sujan; Deo, Ravinesh C.; Casillas-Pérez, David; Salcedo-Sanz, Sancho; Acharya, Rajendra; Dinh, Toan
This work presents a Temporal Convolution Network (TCN) model for half-hourly, three-hourly and daily-time step to predict electricity demand ( ) with associated uncertainties for sites in Southeast Queensland Australia. In addition to multi-step predictions, the TCN model is applied for probabilistic predictions of where the aleatoric and epistemic uncertainties are quantified using maximum likelihood and Monte Carlo Dropout methodologies. The benchmarks of TCN model include an attention-based, bi-directional, gated recurrent unit, seq2seq, encoder–decoder, recurrent neural networks and natural gradient boosting models. The testing results show that the proposed TCN model attains the lowest relative root mean square error of 5.336-7.547% compared with significantly larger errors for all benchmark models. In respect to the 95% confidence interval using the Diebold–Mariano test statistic and key performance metrics, the proposed TCN model is better than benchmark models, capturing a lower value of total uncertainty, as well as the aleatoric and epistemic uncertainty. The root mean square error and total uncertainty registered for all of the forecast horizons shows that the benchmark models registered relatively larger errors arising from the epistemic uncertainty in predicted electricity demand. The results obtained for TCN, measured by the quality of prediction intervals representing an interval with upper and lower bound errors, registered a greater reliability factor as this model was likely to produce prediction interval that were higher than benchmark models at all prediction intervals. These results demonstrate the effectiveness of TCN approach in electricity demand modelling, and therefore advocates its usefulness in now-casting and forecasting systems.
Machine learning regression and classification methods for fog events prediction
(Elsevier, 2022) Castillo-Botón, Carlos; Casillas-Pérez, David; Casanova-Mateo, Carlos; Ghimire, Sujan; Cerro-Prada, Elena; Gutierrez, P.A.; Deo, Ravinesh; Salcedo-Sanz, Sancho
Atmospheric low-visibility events are usually associated with fog formation. Extreme low-visibility events deeply affect the air and ground transportation, airports and motor-road facilities causing accidents and traffic problems every year. Machine Learning (ML) algorithms have been successfully applied to many fog formation and lowvisibility prediction problems. The associated problem can be formulated either as a regression or as a classification task, which has an impact on the type of ML approach to be used and on the quality of the predictions obtained. In this paper we carry out a complete analysis of low-visibility events prediction problems, formulated as both regression and classification problems. We discuss the performance of a large number of ML approaches in each type of problem, and evaluate their performance under a common comparison framework. According to the obtained results, we will provide indications on what the most efficient formulation is to tackle low-visibility predictions and the best performing ML approaches for low-visibility events prediction.
Point-based and probabilistic electricity demand prediction with a Neural Facebook Prophet and Kernel Density Estimation model
(Elsevier, 2024-09) Ghimire, Sujan; Deo, Ravinesh C.; Pourmousavi, S. Ali; Casillas-Pérez, David; Salcedo-Sanz, Sancho
Electricity demand prediction is crucial to ensure the operational safety and cost-efficient operation of the power system. Electricity demand has predominantly been predicted deterministically, while uncertainty analysis has been usually overlooked. To address this research gap, an integrated Neural Facebook Prophet (NFBP) model and Gaussian Kernel Density Estimation (KDE) model is proposed in this paper, as a way to obtain point and interval predictions of electricity demand, quantifying this way the uncertainty in the predictions. First, historical lagged data, created by utilizing the Partial Auto-correlation Function and Mutual Information Test, is applied to train a prediction model based on NFBP, Deep Learning (DL) as well as Statistical Models. Second, the model Prediction Errors (PE) are derived from the difference between actual and predicted values. A splitting strategy based on the mean and standard deviation of PE is proposed. Finally, electricity demand prediction intervals are obtained by applying Gaussian KDE on split PE. To verify the effectiveness of the proposed model, simulation studies are carried out for three prediction horizons on freely available datasets for the Bulimba sub-station in Southeast Queensland, Australia. Compared with DL models (Long-Short Term Memory Network and Deep Neural Network), the Root Mean Square Error of the NFBP model was reduced by 6.1% and 11.3% for 0.5-hr ahead, 22.7% and 26.3% for 6-hr ahead, and 31.8% and 29.9% for daily prediction. In addition, the Prediction Interval normalized Interval width is smaller in magnitude for the proposed NFBP-KDE model compared to other DL and Statistical models
Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting
(Elsevier, 2023) Ghimire, Sujan; Deo, Ravinesh C.; Casillas-Pérez, David; Salcedo-Sanz, Sancho
Prediction of electricity price is crucial for national electricity markets supporting sale prices, bidding strategies, electricity dispatch, control and market volatility management. High volatility, non-stationarity and multi-seasonality of electricity prices make it significantly challenging to estimate its future trend, especially over near real-time forecast horizons. An error compensation strategy that integrates Long ShortTerm Memory (LSTM) network, Convolution Neural Network (CNN) and the Variational Mode Decomposition (VMD) algorithm is proposed to predict the half-hourly step electricity prices. A prediction model incorporating VMD and CLSTM is first used to obtain an initial prediction. To improve its predictive accuracy, a novel error compensation framework, which is built using the VMD and a Random Forest Regression (RF) algorithm, is also used. The proposed VMD-CLSTM-VMD-ERCRF model is evaluated using electricity prices from Queensland, Australia. The results reveal highly accurate predictive performance for all datasets considered, including the winter, autumn, spring, summer, and yearly predictions. As compared with a predictive model without error compensation (i.e., the VMD-CLSTM model), the proposed VMD-CLSTM-VMD-ERCRF model outperforms the benchmark models. For winter, autumn, spring, summer, and yearly predictions, the average Legates and McCabe Index is seen to increase by 15.97%, 16.31%, 20.23%, 10.24%, and 14.03%, respectively, relative to the benchmark models. According to the tests performed on independent datasets, the proposed VMD-CLSTMVMD-ERCRF model can be a practical stratagem useful for short-term, half-hourly electricity price forecasting. Therefore the research outcomes demonstrate that the proposed error compensation framework is an effective decision-support tool for improving the predictive accuracy of electricity price. It could be of practical value to energy companies, energy policymakers and national electricity market operators to develop their insight analysis, electricity distribution and market optimization strategies.